Journal of Sea Research 198 (2024) 102472 Journal of Sea Research
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Region
Coordinates
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Tangier
36.250206152534847 - 5.977397294819712
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Agadir
31.14608750019331 - 9.66901533235119
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Casablanca
33.07723556735074 - 7.843945740762594
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Kariat Arkmane
35.1203952992372 - 2.734379316487201
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Parameter
Explanation
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x i
The input vector at time t (i.e., SST time series)
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f i
The output of the first layer
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h i
The output of the input gate
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c i
The vector of candidate values that should be added to the cell state c i
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a i
The output of the output state
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h k
The hidden state at time t
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y k
The final predicted output at time t
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1
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Hyperparameter
Value
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Input shape
(3593, 60, 1)
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Units
128-128-64
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Batch size
32
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Learning rate
10 - 4
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Optimizer
Adam
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Epochs
150
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Loss function
Mean Squared Error
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Dropout
0.2
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Kernel initializer
Glorot uniform
Model
Number of BILSTM layers
Number of dropout layers
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Model A
3
0
1
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1
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Model C
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Model D
3
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Model
MAE
MAPE
RMSE
R 2
Training
Inference
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XGBoost
0.3104
0.01715
0.4442
0.95414
0.11
0.01
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RF
0.3154
0.01746
0.45435
0.95202
14.09
0.01
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0.298
0.01649
0.42458
0.9581
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LSTM
0.3005
0.01671
0.43193
0.9557
497.14
1.48
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BiLSTM
0.2982
0.01648
0.4208
0.95795
487.34
1.45
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Attention-BiGRU
0.2995
0.01655
0.42433
0.95724
357.57
1.75
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Transformers
0.3388
0.01895
0.4649
0.94868
387.79
1.47
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Attention-BiLSTM
0.3095
0.0172
0.42222
0.95767
453.72
2.03
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N. Zrina et al.
N. Zrina et al.
N. Zrina et al.
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Predicting results of Attention-BiLSTM on Tangier.
Predicting results of Attention-BiLSTM on Tangier.
Predicting results of Attention-BiLSTM on Tangier.
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Predicting results of Attention-BiLSTM on Kariat Arkmane.
Predicting results of Attention-BiLSTM on Kariat Arkmane.
Predicting results of Attention-BiLSTM on Kariat Arkmane.
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Predicting results of Attention-BiLSTM on Casablanca.
Predicting results of Attention-BiLSTM on Casablanca.
Predicting results of Attention-BiLSTM on Casablanca.
0
0
N. Zrira et al.
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extension to all coastal cities considerably increases the complexity of tuning hyperparameters in deep learning models. Additionally, climate change emerges as a complex and influential factor impacting predictive models due to changes in global or regional weather patterns. The prediction of our model is very close to reality because it is currently
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learmed on stable data. One of the most obvious impacts of climate change is increased SST. The oceans will absorb much of the excess heat trapped by greenhouse gases, causing SST to increase over time. In this case, our model will not be able to predict future SST values well.
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In future work, we plan to explore the application of the neural prophet model to Moroccan SST data as well as other marine datasets such as Pirata (the Prediction and Research Moored Array in the Atlantic). By testing the neural prophet model, we aim to further enhance our understanding and predictive capabilities in the domain of SST forecasting. This will contribute to the advancement of marine
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Credit authorship contribution statement
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Nabila Zrira: Writing - review & editing. Writing - original draft. Methodology. Investigation. Data curation. Conceptualization. Assia Kamal-Idrissi: Writing - original draft, Validation, Methodology, Investigation. Formal analysis. Conceptualization. Rahma Farssi: Writing - review & editing. Writing - original draft, Conceptualization. Haris Ahmad Khan: Writing - review & editing. Writing - original
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1
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N. Zrina et al.
Journal of Sea Research 198 (2024) 103472
1
2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 1480-1489.
